MSc thesis subject: Use of machine learning for detecting deforestation in dense Sentinel-1 SAR time series (Study site: Sumatra/Bolivia)

With Sentinel-1A and -1B (launch 2014 and 2016) for the first time, dense and regular SAR time series data are provided over tropical forest areas free and openly. Such potential needs to be utilized.

This thesis will explore the potential of machine learning (e.g. random forest) to detect new deforestation events in dense Sentinel-1 time series.

Timely information on deforestation is crucial to effectively manage and protect forest resources in the tropics. Monitoring of changes in tropical forest cover has relied predominantly on optical satellite sensors, however persistent cloud cover limits its use for near real-time forest change detection in many tropical regions. Spaceborne SAR data have the advantage of providing cloud-free observations. With Sentinel-1 (launch 2014) for the first time, dense and regular SAR time series data are provided over tropical forest areas free and openly.

Most of the world's oil palm trees are grown on a few islands in Malaysia and Indonesia – islands with the most biodiverse tropical forests found on Earth, and where there is a direct relationship between the growth of oil palm estates and deforestation. Satellite-based near real-time monitoring is the only tool capable of providing timely information on new (illegal) deforested areas in the palm oil area of Indonesia and Malaysia.

The aim of this research topic is to explore the potential of machine learning (e.g. random forest) to detect new deforestation events in dense Sentinel-1 time series. Method development will be done at a study site in Sumatra covering a major palm oil area or a dry tropical forest site in Bolivia affected by large scale logging activities. The combination and/or comparison with Landsat time series may be considered. Very high resolution reference data from Planet.com are available for validation.

Objectives

Explore machine learning for detecting deforestation in dense Sentinel-1 C-band SAR time series